{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples \n",
    "\n",
    "This notebook gives example of all 3 algrorithms implemented, along with some utlity functions\n",
    "\n",
    "1. Linear Gaussian\n",
    "2. Linear Gaussian Experimental\n",
    "3. Gaussian Belief Propogation (GaussianBP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lgnpy import LinearGaussian\n",
    "from lgnpy import GaussianBP\n",
    "from lgnpy import LinearGaussianExperimental\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Objects for all 3 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg1 = LinearGaussian()\n",
    "lg2 = LinearGaussianExperimental()\n",
    "lg3 = GaussianBP()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg1.set_edges_from([('A', 'E'), ('B', 'E'), ('E', 'F'),('E','G'),('D', 'G'),('C', 'H'),('H', 'G')])\n",
    "lg2.set_edges_from([('A', 'E'), ('B', 'E'), ('E', 'F'),('E','G'),('D', 'G'),('C', 'H'),('H', 'G')])\n",
    "lg3.set_edges_from([('A', 'E'), ('B', 'E'), ('E', 'F'),('E','G'),('D', 'G'),('C', 'H'),('H', 'G')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create simulated Data for network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=100\n",
    "data = pd.DataFrame(columns=['A','B','C','D','E'])\n",
    "\n",
    "#Root nodes data\n",
    "data['A'] = np.random.normal(5,2,n)\n",
    "data['B'] = np.random.normal(10,2,n)\n",
    "data['C'] = np.random.normal(50,2,n)\n",
    "data['D'] = np.random.normal(20,2,n)\n",
    "\n",
    "#Data for nodes with parents\n",
    "data['E'] = 2*data['A'] + 3*data['B'] + np.random.normal(3,5,n)\n",
    "data['F'] = 3*data['E'] + np.random.normal(-5,2,n)\n",
    "data['H'] = 0.4*data['C'] + np.random.normal(0,2,n)\n",
    "data['G'] = 5 + 3*data['E'] + 0.5*data['H'] + 0.2*data['D'] +  np.random.normal(0,5,n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg1.set_data(data)\n",
    "lg2.set_data(data)\n",
    "lg3.set_data(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lg1.draw_network(\"my_network\") #Only first drawn, because all are same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Evidences (if any)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg1.set_evidences({'A':5,'B':10})\n",
    "lg2.set_evidences({'A':5,'B':10})\n",
    "lg3.set_evidences({'A':5,'B':10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run inference for each one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Started\n",
      "Calculated:'E'= 42.63\n",
      "Parent nodes used: {'A': 5, 'B': 10}\n",
      "Beta calculated: [-0.62, 1.67, 3.49]\n",
      "Calculated:'H'= 20.1\n",
      "Parent nodes used: {'C': 50.13}\n",
      "Beta calculated: [5.64, 0.29]\n",
      "Calculated:'G'= 146.954\n",
      "Parent nodes used: {'E': 42.63, 'D': 20.214, 'H': 20.1}\n",
      "Beta calculated: [13.93, 3.01, -0.02, 0.26]\n",
      "Calculated:'F'= 122.651\n",
      "Parent nodes used: {'E': 42.63}\n",
      "Beta calculated: [-3.38, 2.96]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Evidence</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Mean_inferred</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Variance_inferred</th>\n",
       "      <th>u_%change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>4.7923</td>\n",
       "      <td>5</td>\n",
       "      <td>3.299</td>\n",
       "      <td></td>\n",
       "      <td>4.3339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0446</td>\n",
       "      <td>10</td>\n",
       "      <td>3.638</td>\n",
       "      <td></td>\n",
       "      <td>-0.4441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td></td>\n",
       "      <td>50.1298</td>\n",
       "      <td></td>\n",
       "      <td>4.703</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>D</td>\n",
       "      <td></td>\n",
       "      <td>20.2137</td>\n",
       "      <td></td>\n",
       "      <td>3.127</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>E</td>\n",
       "      <td></td>\n",
       "      <td>42.4384</td>\n",
       "      <td>42.6299</td>\n",
       "      <td>74.904</td>\n",
       "      <td>26.9087</td>\n",
       "      <td>0.4512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td>122.0846</td>\n",
       "      <td>122.651</td>\n",
       "      <td>657.929</td>\n",
       "      <td>3.269</td>\n",
       "      <td>0.4636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>G</td>\n",
       "      <td></td>\n",
       "      <td>146.3772</td>\n",
       "      <td>146.954</td>\n",
       "      <td>706.643</td>\n",
       "      <td>23.2971</td>\n",
       "      <td>0.3939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>H</td>\n",
       "      <td></td>\n",
       "      <td>20.1001</td>\n",
       "      <td>20.1001</td>\n",
       "      <td>4.888</td>\n",
       "      <td>4.4972</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Evidence      Mean Mean_inferred  Variance Variance_inferred u_%change\n",
       "A        5    4.7923             5     3.299                      4.3339\n",
       "B       10   10.0446            10     3.638                     -0.4441\n",
       "C            50.1298                   4.703                            \n",
       "D            20.2137                   3.127                            \n",
       "E            42.4384       42.6299    74.904           26.9087    0.4512\n",
       "F           122.0846       122.651   657.929             3.269    0.4636\n",
       "G           146.3772       146.954   706.643           23.2971    0.3939\n",
       "H            20.1001       20.1001     4.888            4.4972          "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.run_inference(debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experimental Linear Gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Started\n",
      "Recursing for D with D\n",
      "Recursing for G with D\n",
      "Recursing for E with G\n",
      "Calculated:'E'= 42.449\n",
      "Parent nodes used: {'A': 5, 'B': 10, 'F': 122.085}\n",
      "Beta calculated: [0.96, 0.06, 0.06, 0.33]\n",
      "Recursing for H with G\n",
      "Calculated:'H'= 20.1\n",
      "Parent nodes used: {'C': 50.13}\n",
      "Beta calculated: [5.64, 0.29]\n",
      "Calculated:'G'= 146.409\n",
      "Parent nodes used: {'E': 42.449, 'H': 20.1}\n",
      "Beta calculated: [13.45, 3.01, 0.26]\n",
      "Calculated:'D'= 20.214\n",
      "Parent nodes used: {'G': 146.409}\n",
      "Beta calculated: [19.76, 0.0]\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Evidence</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Mean_inferred</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Variance_inferred</th>\n",
       "      <th>u_%change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>4.7923</td>\n",
       "      <td>5</td>\n",
       "      <td>3.299</td>\n",
       "      <td></td>\n",
       "      <td>4.3339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0446</td>\n",
       "      <td>10</td>\n",
       "      <td>3.638</td>\n",
       "      <td></td>\n",
       "      <td>-0.4441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td></td>\n",
       "      <td>50.1298</td>\n",
       "      <td></td>\n",
       "      <td>4.703</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>D</td>\n",
       "      <td></td>\n",
       "      <td>20.2137</td>\n",
       "      <td>20.2138</td>\n",
       "      <td>3.127</td>\n",
       "      <td>3.1197</td>\n",
       "      <td>0.0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>E</td>\n",
       "      <td></td>\n",
       "      <td>42.4384</td>\n",
       "      <td>42.449</td>\n",
       "      <td>74.904</td>\n",
       "      <td>0.3612</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td>122.0846</td>\n",
       "      <td></td>\n",
       "      <td>657.929</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>G</td>\n",
       "      <td></td>\n",
       "      <td>146.3772</td>\n",
       "      <td>146.409</td>\n",
       "      <td>706.643</td>\n",
       "      <td>23.299</td>\n",
       "      <td>0.0218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>H</td>\n",
       "      <td></td>\n",
       "      <td>20.1001</td>\n",
       "      <td>20.1001</td>\n",
       "      <td>4.888</td>\n",
       "      <td>4.4972</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Evidence      Mean Mean_inferred  Variance Variance_inferred u_%change\n",
       "A        5    4.7923             5     3.299                      4.3339\n",
       "B       10   10.0446            10     3.638                     -0.4441\n",
       "C            50.1298                   4.703                            \n",
       "D            20.2137       20.2138     3.127            3.1197    0.0005\n",
       "E            42.4384        42.449    74.904            0.3612     0.025\n",
       "F           122.0846                 657.929                            \n",
       "G           146.3772       146.409   706.643            23.299    0.0218\n",
       "H            20.1001       20.1001     4.888            4.4972          "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg2.run_inference('D',debug=True) #This experimental runs on particular node only. Ignore Duplicate log messages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gaussian Beilief Propogation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Did not converge in given 100. You can use plot_errors() method to see convergence plot.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Evidence</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Mean_inferred</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Variance_inferred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.792307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.299080</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.044609</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.637938</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.129793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.702677</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.213680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.126541</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.438419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.903705</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.084646</td>\n",
       "      <td>NaN</td>\n",
       "      <td>657.928687</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146.377243</td>\n",
       "      <td>NaN</td>\n",
       "      <td>706.642613</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.100105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.888273</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Evidence        Mean Mean_inferred    Variance Variance_inferred\n",
       "A       5.0    4.792307           NaN    3.299080               NaN\n",
       "B      10.0   10.044609           NaN    3.637938               NaN\n",
       "C       NaN   50.129793           NaN    4.702677               NaN\n",
       "D       NaN   20.213680           NaN    3.126541               NaN\n",
       "E       NaN   42.438419           NaN   74.903705               NaN\n",
       "F       NaN  122.084646           NaN  657.928687               NaN\n",
       "G       NaN  146.377243           NaN  706.642613               NaN\n",
       "H       NaN   20.100105           NaN    4.888273               NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg3.run_inference(iterations=100) #Dint converge for this graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lg1.plot_distributions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Node</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Std</th>\n",
       "      <th>Parents</th>\n",
       "      <th>Children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>4.7923</td>\n",
       "      <td>1.8163</td>\n",
       "      <td>[]</td>\n",
       "      <td>[E]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>10.0446</td>\n",
       "      <td>1.9073</td>\n",
       "      <td>[]</td>\n",
       "      <td>[E]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>50.1298</td>\n",
       "      <td>2.1686</td>\n",
       "      <td>[]</td>\n",
       "      <td>[H]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>D</td>\n",
       "      <td>20.2137</td>\n",
       "      <td>1.7682</td>\n",
       "      <td>[]</td>\n",
       "      <td>[G]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>E</td>\n",
       "      <td>42.4384</td>\n",
       "      <td>8.6547</td>\n",
       "      <td>[A, B]</td>\n",
       "      <td>[F, G]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>F</td>\n",
       "      <td>122.0846</td>\n",
       "      <td>25.6501</td>\n",
       "      <td>[E]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>G</td>\n",
       "      <td>146.3772</td>\n",
       "      <td>26.5828</td>\n",
       "      <td>[E, D, H]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>H</td>\n",
       "      <td>20.1001</td>\n",
       "      <td>2.2109</td>\n",
       "      <td>[C]</td>\n",
       "      <td>[G]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Node      Mean      Std    Parents Children\n",
       "0    A    4.7923   1.8163         []      [E]\n",
       "1    B   10.0446   1.9073         []      [E]\n",
       "2    C   50.1298   2.1686         []      [H]\n",
       "3    D   20.2137   1.7682         []      [G]\n",
       "4    E   42.4384   8.6547     [A, B]   [F, G]\n",
       "5    F  122.0846  25.6501        [E]       []\n",
       "6    G  146.3772  26.5828  [E, D, H]       []\n",
       "7    H   20.1001   2.2109        [C]      [G]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.network_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lg3.plot_errors() #Only aviabble for GaussianBP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Evidence</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Mean_inferred</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Variance_inferred</th>\n",
       "      <th>u_%change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>4.7923</td>\n",
       "      <td>5</td>\n",
       "      <td>3.299</td>\n",
       "      <td></td>\n",
       "      <td>4.3339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0446</td>\n",
       "      <td>10</td>\n",
       "      <td>3.638</td>\n",
       "      <td></td>\n",
       "      <td>-0.4441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td></td>\n",
       "      <td>50.1298</td>\n",
       "      <td></td>\n",
       "      <td>4.703</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>D</td>\n",
       "      <td></td>\n",
       "      <td>20.2137</td>\n",
       "      <td></td>\n",
       "      <td>3.127</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>E</td>\n",
       "      <td></td>\n",
       "      <td>42.4384</td>\n",
       "      <td>42.6299</td>\n",
       "      <td>74.904</td>\n",
       "      <td>26.9087</td>\n",
       "      <td>0.4512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td>122.0846</td>\n",
       "      <td>122.651</td>\n",
       "      <td>657.929</td>\n",
       "      <td>3.269</td>\n",
       "      <td>0.4636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>G</td>\n",
       "      <td></td>\n",
       "      <td>146.3772</td>\n",
       "      <td>146.954</td>\n",
       "      <td>706.643</td>\n",
       "      <td>23.2971</td>\n",
       "      <td>0.3939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>H</td>\n",
       "      <td></td>\n",
       "      <td>20.1001</td>\n",
       "      <td>20.1001</td>\n",
       "      <td>4.888</td>\n",
       "      <td>4.4972</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Evidence      Mean Mean_inferred  Variance Variance_inferred u_%change\n",
       "A        5    4.7923             5     3.299                      4.3339\n",
       "B       10   10.0446            10     3.638                     -0.4441\n",
       "C            50.1298                   4.703                            \n",
       "D            20.2137                   3.127                            \n",
       "E            42.4384       42.6299    74.904           26.9087    0.4512\n",
       "F           122.0846       122.651   657.929             3.269    0.4636\n",
       "G           146.3772       146.954   706.643           23.2971    0.3939\n",
       "H            20.1001       20.1001     4.888            4.4972          "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.inf_summary #Get summary of inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>3.299080</td>\n",
       "      <td>-0.472617</td>\n",
       "      <td>0.751689</td>\n",
       "      <td>-0.546709</td>\n",
       "      <td>3.864676</td>\n",
       "      <td>11.082491</td>\n",
       "      <td>10.623449</td>\n",
       "      <td>0.451469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B</td>\n",
       "      <td>-0.472617</td>\n",
       "      <td>3.637938</td>\n",
       "      <td>-0.151516</td>\n",
       "      <td>-0.059400</td>\n",
       "      <td>11.903884</td>\n",
       "      <td>35.254452</td>\n",
       "      <td>36.730528</td>\n",
       "      <td>0.281298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td>0.751689</td>\n",
       "      <td>-0.151516</td>\n",
       "      <td>4.702677</td>\n",
       "      <td>-0.000993</td>\n",
       "      <td>-0.174694</td>\n",
       "      <td>-1.133052</td>\n",
       "      <td>-0.049114</td>\n",
       "      <td>1.356046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>D</td>\n",
       "      <td>-0.546709</td>\n",
       "      <td>-0.059400</td>\n",
       "      <td>-0.000993</td>\n",
       "      <td>3.126541</td>\n",
       "      <td>0.743318</td>\n",
       "      <td>2.467016</td>\n",
       "      <td>2.199467</td>\n",
       "      <td>0.152563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>E</td>\n",
       "      <td>3.864676</td>\n",
       "      <td>11.903884</td>\n",
       "      <td>-0.174694</td>\n",
       "      <td>0.743318</td>\n",
       "      <td>74.903705</td>\n",
       "      <td>221.441725</td>\n",
       "      <td>226.188907</td>\n",
       "      <td>2.555842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>F</td>\n",
       "      <td>11.082491</td>\n",
       "      <td>35.254452</td>\n",
       "      <td>-1.133052</td>\n",
       "      <td>2.467016</td>\n",
       "      <td>221.441725</td>\n",
       "      <td>657.928687</td>\n",
       "      <td>669.370855</td>\n",
       "      <td>7.095005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>G</td>\n",
       "      <td>10.623449</td>\n",
       "      <td>36.730528</td>\n",
       "      <td>-0.049114</td>\n",
       "      <td>2.199467</td>\n",
       "      <td>226.188907</td>\n",
       "      <td>669.370855</td>\n",
       "      <td>706.642613</td>\n",
       "      <td>8.946494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>H</td>\n",
       "      <td>0.451469</td>\n",
       "      <td>0.281298</td>\n",
       "      <td>1.356046</td>\n",
       "      <td>0.152563</td>\n",
       "      <td>2.555842</td>\n",
       "      <td>7.095005</td>\n",
       "      <td>8.946494</td>\n",
       "      <td>4.888273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A          B         C         D           E           F  \\\n",
       "A   3.299080  -0.472617  0.751689 -0.546709    3.864676   11.082491   \n",
       "B  -0.472617   3.637938 -0.151516 -0.059400   11.903884   35.254452   \n",
       "C   0.751689  -0.151516  4.702677 -0.000993   -0.174694   -1.133052   \n",
       "D  -0.546709  -0.059400 -0.000993  3.126541    0.743318    2.467016   \n",
       "E   3.864676  11.903884 -0.174694  0.743318   74.903705  221.441725   \n",
       "F  11.082491  35.254452 -1.133052  2.467016  221.441725  657.928687   \n",
       "G  10.623449  36.730528 -0.049114  2.199467  226.188907  669.370855   \n",
       "H   0.451469   0.281298  1.356046  0.152563    2.555842    7.095005   \n",
       "\n",
       "            G         H  \n",
       "A   10.623449  0.451469  \n",
       "B   36.730528  0.281298  \n",
       "C   -0.049114  1.356046  \n",
       "D    2.199467  0.152563  \n",
       "E  226.188907  2.555842  \n",
       "F  669.370855  7.095005  \n",
       "G  706.642613  8.946494  \n",
       "H    8.946494  4.888273  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.get_covariance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.46644377,  0.28927171, -0.06389771,  0.10290332, -0.164653  ,\n",
       "         0.01893218,  0.01251737, -0.00951069],\n",
       "       [ 0.28927171,  0.76674973, -0.02730804,  0.09676011, -0.11950257,\n",
       "         0.00655705, -0.01258949,  0.00972243],\n",
       "       [-0.06389771, -0.02730804,  0.24486236, -0.01406874, -0.07447988,\n",
       "         0.03351708, -0.00469419, -0.06112939],\n",
       "       [ 0.10290332,  0.09676011, -0.01406874,  0.34815015,  0.0390211 ,\n",
       "        -0.02499424,  0.003688  , -0.01290951],\n",
       "       [-0.164653  , -0.11950257, -0.07447988,  0.0390211 ,  3.07136748,\n",
       "        -0.92159112, -0.10104126, -0.0417887 ],\n",
       "       [ 0.01893218,  0.00655705,  0.03351708, -0.02499424, -0.92159112,\n",
       "         0.32166872, -0.01055541,  0.0236494 ],\n",
       "       [ 0.01251737, -0.01258949, -0.00469419,  0.003688  , -0.10104126,\n",
       "        -0.01055541,  0.0443661 , -0.01229305],\n",
       "       [-0.00951069,  0.00972243, -0.06112939, -0.01290951, -0.0417887 ,\n",
       "         0.0236494 , -0.01229305,  0.23227325]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.get_precision_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A      4.792307\n",
       "B     10.044609\n",
       "C     50.129793\n",
       "D     20.213680\n",
       "E     42.438419\n",
       "F    122.084646\n",
       "G    146.377243\n",
       "H     20.100105\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.get_mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': None,\n",
       " 'B': None,\n",
       " 'C': None,\n",
       " 'D': None,\n",
       " 'E': {'node_values': {'A': 5, 'B': 10}, 'node_betas': [-0.62, 1.67, 3.49]},\n",
       " 'F': {'node_values': {'E': 42.63}, 'node_betas': [-3.38, 2.96]},\n",
       " 'G': {'node_values': {'E': 42.63, 'D': 20.214, 'H': 20.1},\n",
       "  'node_betas': [13.93, 3.01, -0.02, 0.26]},\n",
       " 'H': {'node_values': {'C': 50.13}, 'node_betas': [5.64, 0.29]}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.get_model_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'E', 'B', 'F', 'G', 'D', 'C', 'H']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.get_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('A', 'E'),\n",
       " ('E', 'F'),\n",
       " ('E', 'G'),\n",
       " ('B', 'E'),\n",
       " ('D', 'G'),\n",
       " ('C', 'H'),\n",
       " ('H', 'G')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.get_edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.get_parents('E')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F', 'G']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.get_children('E')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['E', 'D', 'H']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.get_siblings('E')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'F', 'G']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.get_neighbors('E')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.has_children('E')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.has_parents('D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg1.set_edge('A','B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions related to Evidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 5,\n",
       " 'B': 10,\n",
       " 'C': None,\n",
       " 'D': None,\n",
       " 'E': None,\n",
       " 'F': None,\n",
       " 'G': None,\n",
       " 'H': None}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.get_evidences()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': None,\n",
       " 'B': None,\n",
       " 'C': None,\n",
       " 'D': None,\n",
       " 'E': None,\n",
       " 'F': None,\n",
       " 'G': None,\n",
       " 'H': None}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg1.clear_evidences() #Clears evidence from network\n",
    "lg1.get_evidences() #Check if evidences are cleared"
   ]
  }
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